| ## Demo of SimCSE | |
| Several demos are available for people to play with our pre-trained SimCSE. | |
| ### Flask Demo | |
| <div align="center"> | |
| <img src="../figure/demo.gif" width="750"> | |
| </div> | |
| We provide a simple Web demo based on [flask](https://github.com/pallets/flask) to show how SimCSE can be directly used for information retrieval. The code is based on [DensePhrases](https://arxiv.org/abs/2012.12624)' [repo](https://github.com/princeton-nlp/DensePhrases) and [demo](http://densephrases.korea.ac.kr) (a lot of thanks to the authors of DensePhrases). To run this flask demo locally, make sure the SimCSE inference interfaces are setup: | |
| ```bash | |
| git clone https://github.com/princeton-nlp/SimCSE | |
| cd SimCSE | |
| python setup.py develop | |
| ``` | |
| Then you can use `run_demo_example.sh` to launch the demo. As a default setting, we build the index for 1000 sentences sampled from STS-B dataset. Feel free to build the index of your own corpora. You can also install [faiss](https://github.com/facebookresearch/faiss) to speed up the retrieval process. | |
| ### Gradio Demo | |
| [AK391](https://github.com/AK391) has provided a [Gradio Web Demo](https://gradio.app/g/AK391/SimCSE) of SimCSE to show how the pre-trained models can predict the semantic similarity between two sentences. | |